MFB: Targeting the Dark Proteome by Machine-learning-guided Protein Design

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Drs. Sagar Khare, Jean Baum, Adam Gormley, Guillaume Lamoureux, and Sijian Wang from Rutgers University will develop targeted protein editors guided by novel machine learning (ML) approaches. The ability to precisely edit genomes has transformed modern biotechnology and medicine; however, technology for the in-situ precision editing of proteins – the workhorses of biology – has been lacking. Targeting with such methods proteins contain functionally-important but structurally-disordered segments would be particularly useful but it is challenging. The interdisciplinary research team will develop and apply novel machine learning algorithms to the design of new editor proteins that contain a binding domain and an enzymatic domain to ensure the selective recognition and modification, respectively, of a chosen target protein in the presence of thousands other proteins present in the cell. Iterative Design-Build-Test-Learn cycles will involve a close interplay of biochemical experiments and model development to obtain robust, generalizable and interpretable ML models that predictively enable selective protein editing. The project lies at the interface of chemistry, biophysics, robotics, computer science, and statistics, and will therefore offer unique training and research opportunities for students from both quantitative and biological and chemical science backgrounds. Teams of undergraduates (recruited from various Research Experiences for Undergraduates programs at Rutgers) will use gamified versions of computational protein design algorithms to develop and refine protein editors. Hands-on protein design sessions will be held at affinity group conferences to reach students from groups underrepresented in STEM and to present them with opportunities to learn about, and participate in, research.New ML approaches for protein modeling have recently demonstrated that sequence and structure data can be leveraged to correctly learn complex sequence-structure relationships and design novel proteins. However, abundant functional and biophysical data, e.g., protein-peptide binding data, remain largely unexploited. New ML models that go beyond sequence representations and towards semantically richer representations based on molecular structure are needed. These representations can capture more specific sequence-function and sequence-energetics relationships. The research team will use a Design-Build-Test-Learn pipeline driven by ML models that are trained on both experimental data and molecular structure and energetics. The team will apply these methods to the design of PDZ domains that bind with high affinity and specificity to the C-termini of target intrinsically disordered proteins and selectively cleave at an internal sequence site in the targeted protein. ML models will be pre-trained on available sequence and experimental biophysical data on PDZ and protease domains and used for generating new proteins. Design validation and iterative improvements to the ML models will be carried out by experimental characterization using high-throughput assays, deep sequencing, biomolecular NMR and robotics-enabled assays of binding and cleavage. Successful designed proteins will be fused to obtain PDZ-protease protein editors. The developed methodology will be general and broadly applicable to allow in situ protein editing of a variety of biologically relevant targets, especially with intrinsically disordered proteins or regions. The novel reagents will help illuminate the so-called “dark” proteome. This project is supported by the Division of Chemistry (CHE) and the Division of Mathematical Sciences (DMS) in the Mathematics and Physical Sciences (MPS) Directorate and by the the Division of Information and Intelligent Systems (IIS) in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.